Samsung SAIL Montréal
Summary: Samsung AI Lab in Montréal (SAIL = Samsung AI Lab). Produced HRM (Hierarchical Reasoning Model) and TRM (Tiny Recursive Model) — recursive reasoning architectures achieving SOTA on ARC-AGI with tiny parameter counts (7M-27M) via deep supervision + ACT. Key researcher: Alexia Jolicoeur-Martineau.
Details
For Labs
- Type: Industrial Research Lab (Samsung)
- Location: Montréal, Canada
- Key Research: Recursive reasoning, deep supervision, ARC-AGI, ACT, small-model reasoning
- Key Papers: HRM (2025), TRM (2025)
- Lead Researcher: Alexia Jolicoeur-Martineau
- Website: samsonailabs.com (Samsung AI Labs)
Significance
Research alignment: Your interest in recursive reasoning, latent space reasoning, ACT, deep supervision directly matches their output.
| HRM/TRM Innovation | Your Interest Match |
|---|---|
| Deep supervision (primary driver, 19%→39%) | Core mechanism for your recursive models |
| ACT with Q-learning halting | Latent space reasoning + adaptive compute |
| 1-step gradient (IFT + Neumann) | Efficient training for deep recursion |
| 7M-27M params beating LLMs on ARC | Small-model reasoning — very relevant |
PhD relevance:
- Academic + industry hybrid (Samsung + Mila/Université de Montréal ecosystem)
- Publishes at top venues (HRM/ICLR, TRM/arXiv)
- Small team → potential for direct mentorship
- Montréal = top AI hub (Mila, McGill, UdeM, Google DeepMind, Meta FAIR, Microsoft)
Target lab assessment: Tier 1. Perfect match for recursive reasoning / ACT / deep supervision research. Alexia Jolicoeur-Martineau is emerging star.
Key Papers
| Paper | Year | Innovation | Your Wiki |
|---|---|---|---|
| HRM: Hierarchical Reasoning Model | 2025 | H/L hierarchy, deep supervision, ACT | paper-hrm |
| TRM: Tiny Recursive Model | 2025 | Single tiny net, deep supervision is key | paper-trm |
Key People
| Person | Role | Your Wiki |
|---|---|---|
| Alexia Jolicoeur-Martineau | Lead Researcher (HRM, TRM) | alexia-jolicoeur-martineau |
Research Directions (from papers)
- Deep supervision as primary driver — TRM ablation shows hierarchy adds little; deep supervision is key
- ACT stability via Post-Norm + AdamW — avoids replay buffers/target networks
- Inference-time scaling via ACT — train M_max=8, test M_max=16 zero-shot
- Flat recursion > hierarchy — TRM beats HRM with 4× fewer params
- Small models for reasoning — 7M params competitive with 7B LLMs on ARC
Contact Strategy
Timing: Pre-application (Fall 2025) — reach out Summer 2025 with paper/idea Angle: Your DSA-ViT work + interest in recursive vision reasoning + ACT for visual tasks Hook: TRM removes hierarchy but keeps deep supervision → apply to ViT? Visual ARC? Video reasoning?
Related Wiki Pages
- paper-hrm — HRM full breakdown
- paper-trm — TRM full breakdown
- hrm-reasoning — Architecture concept
- trm-recursive-reasoning — Architecture concept
- deep-supervision — Core training mechanism
- act-adaptive-computation — Halting mechanism
- alexia-jolicoeur-martineau — Lead researcher profile
- target-labs — Your PhD target list
Sources
- arxiv-2510.04871: TRM paper (affiliation)
- arxiv-2506.21734: HRM paper (affiliation)